Service Management and Orchestration (smo) Configurations for External Functionalities

This disclosure generally relates to wireless communication systems and, more particularly, to techniques to configure service management and orchestration (SMO) for external functionalities.

4 5 6 Wireless communications systems are widely deployed to provide various types of communication content such as voice, video, packet data, messaging, broadcast, and so on. A wireless multiple-access communications system may include a number of network nodes, base stations or network access nodes, each simultaneously supporting communication for multiple communication devices, which may be otherwise known as user equipment (UE). These systems may be capable of supporting communication with multiple UEs by sharing the available system resources (such as time, frequency, and power). Examples of such multiple-access systems include fourth generation (G) systems such as Long Term Evolution (LTE) systems, LTE-Advanced (LTE-A) systems, or LTE-A Pro systems, fifth generation (G) systems which may be referred to as New Radio (NR) systems, and sixth generation (G) systems. These systems may employ technologies such as code division multiple access (CDMA), time division multiple access (TDMA), frequency division multiple access (FDMA), orthogonal frequency division multiple access (OFDMA), or discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-S-OFDM). To save network energy or use other services, the systems may include predefined components.

As the demand for mobile broadband access continues to increase, the complexity of network operations increases. Research and development continue to advance wireless communication technologies to automatically manage the network operations. A service management and orchestration (SMO) framework is an automation platform. However, due to the continued wireless communication technology advancement, it is in need to flexibly configure the SMO framework for existing and external functionalities.

The following summarizes some aspects of this disclosure to provide a basic understanding of the discussed technology. This summary is not an extensive overview of all contemplated features of the disclosure and is intended neither to identify key or critical elements of all aspects of the disclosure nor to delineate the scope of any or all aspects of the disclosure. Its sole purpose is to present some concepts of one or more aspects of the disclosure in summary form as a prelude to the more detailed description that is presented later.

This disclosure provides methods, apparatuses, and computer-readable media that support service management and orchestration (SMO) configurations for external functionalities.

In one aspect, a method for wireless communication includes: receiving, by an SMO framework from a first entity, a first request relating to use of an external application; transmitting, by the SMO framework to a second entity, a second request relating to performance of the external application; receiving, by the SMO framework from the second entity, an indication of a result corresponding to the second request transmitted to the second entity; and transmitting an instruction for wireless communication based on the indication.

In another aspect, an apparatus configured to operate as an SMO framework, the apparatus comprising: at least one processor to configure the SMO framework to perform operations comprising: receiving, by the SMO framework from a first entity, a first request relating to use of an external application; transmitting, by the SMO framework to a second entity, a second request relating to performance of the external application; receiving, by the SMO framework from the second entity, an indication of a result corresponding to the second request transmitted to the second entity; and transmitting an instruction for wireless communication based on the indication.

In a further aspect, a computer-readable storage medium that stores instructions for execution by one or more processors of a Service Management and Orchestration (SMO) framework, the instructions to configure the SMO framework to perform operations comprising: receiving, by the SMO framework from a first entity, a first request relating to use of an external application; transmitting, by the SMO framework to a second entity, a second request relating to performance of the external application; receiving, by the SMO framework from the second entity, an indication of a result corresponding to the second request transmitted to the second entity; and transmitting an instruction for wireless communication based on the indication.

The foregoing has outlined rather broadly the features and technical advantages of examples according to the disclosure in order that the detailed description that follows may be better understood. Additional features and advantages will be described hereinafter. The conception and specific examples disclosed may be readily utilized as a basis for modifying or designing other structures for carrying out the same purposes of this disclosure. Such equivalent constructions do not depart from the scope of the appended claims. Characteristics of the concepts disclosed herein, both their organization and method of operation, together with associated advantages will be better understood from the following description when considered in connection with the accompanying figures. Each of the figures is provided for the purposes of illustration and description, and not as a definition of the limits of the claims.

Various aspects of the disclosure are described more fully hereinafter with reference to the accompanying drawings. This disclosure may, however, be embodied in many different forms and are not to be construed as limited to any specific structure or function presented throughout this disclosure. Rather, these aspects are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art. Based on the teachings herein one skilled in the art may appreciate that the scope of the disclosure is intended to cover any aspect of the disclosure disclosed herein, whether implemented independently of or combined with any other aspect of the disclosure. For example, an apparatus may be implemented or a method may be practiced using any quantity of the aspects set forth herein. In addition, the scope of the disclosure is intended to cover such an apparatus or method which is practiced using other structure, functionality, or structure and functionality in addition to or other than the various aspects of the disclosure set forth herein. Any aspect of the disclosure disclosed herein may be embodied by one or more elements of a claim.

As the demand for mobile broadband access continues to increase, the complexity of network operations increases. Research and development continue to advance wireless communication technologies to automatically manage the network operations. The SMO framework is an automation platform to maximize the network’s operational efficiency. At the same time, due to the continued wireless communication technology advancement, it is in need to flexibly configure the SMO framework for existing and external functionalities provided by production grade software frameworks.

This disclosure addresses the need for service management and orchestration (SMO) configurations for and integration with external functionalities to collect data and update control policy or commands for wireless communications. In doing so, this disclosure provides a system or method to configure an SMO framework to operate between third-party foundational services and services within the SMO framework and orchestrate different foundational services by providing an integrated interface to the SMO framework. In addition, this disclosure provides different SMO configurations to serve different scopes and/or time scales. This ensures automatic and reliable wireless communications.

One aspect of this disclosure involves an apparatus configured to operate as an SMO framework. The SMO framework interworks with foundational services providing external functionalities as part of the SMO framework, supports interworking between the foundational services and services within the SMO framework, and/or orchestrates different foundational services by providing an integrated interface to the SMO framework. In addition, this disclosure provides different SMO configurations to serve different scopes (e.g., different domains (radio access network (RAN) or core network (CN)), different time scales (e.g., non-real time and/or near-real time)). For example, the SMO framework receives a first request relating to use of an external application (e.g., an artificial intelligence or machine learning (AI/ML) model) from a first entity (e.g., an rAPP in the SMO framework) and transmits a second request relating to performance of the external application to a second entity (e.g., a third-party foundational service component). Then the SMO framework receives, from the second entity, an indication of a result corresponding to the second request transmitted to the second entity and transmits an instruction (e.g., an instruction to enable a network function (NF) to control the wireless communications) for wireless communication based on the indication.

Particular aspects of the subject matter described in this disclosure can be implemented to realize one or more of the following potential advantages. First, a single SMO framework can be configured to provide different functionalities without replicating existing functionalities in the SMO framework. Second, the SMO framework may not consume unnecessary memory space by not storing existing functionalities in the SMO framework. Rather, the SMO framework reduces the processing power by distributing the processing burden to a third-party entity. By providing a flexible SMO framework to use third party foundational services, a range of network management applications with different reliability and latency requirements can be supported. Also, the single SMO framework merging capabilities for non-real time and near-real time control of the network enables efficient and improved management of the functionalities of non-real time and near-real time control applications.

5 6 As the demand for broadband access increases and as technologies supported by wireless communication networks evolve, further technological improvements may be adopted in or implemented forG NR or future RATs, such asG, to further advance the evolution of wireless communication for a wide variety of existing and new use cases and applications. Such technological improvements may be associated with new frequency band expansion, licensed and unlicensed spectrum access, overlapping spectrum use, small cell deployments, non-terrestrial network (NTN) deployments, disaggregated network architectures and network topology expansion, device aggregation, advanced duplex communication, sidelink and other device-to-device direct communication, IoT (including passive or ambient IoT) networks, reduced capability (RedCap) UE functionality, industrial connectivity, multiple-subscriber implementations, high-precision positioning, radio frequency (RF) sensing, and/or artificial intelligence or machine learning (AI/ML), among other examples. These technological improvements may support use cases such as wireless backhauls, wireless data centers, extended reality (XR) and metaverse applications, meta services for supporting vehicle connectivity, holographic and mixed reality communication, autonomous and collaborative robots, vehicle platooning and cooperative maneuvering, sensing networks, gesture monitoring, human-brain interfacing, digital twin applications, asset management, and universal coverage applications using non-terrestrial and/or aerial platforms, among other examples. The methods, operations, apparatuses, and techniques described herein may enable one or more of the foregoing technologies and/or support one or more of the foregoing use cases.

1 FIG. 100 100 100 110 110 110 110 110 110 120 120 120 120 120 120 a b c d a b c d e is a diagram illustrating an example of a wireless communication network, in accordance with the present disclosure. The wireless communication networkmay be or may include elements of a 5G (or NR) network or a 6G network, among other examples. The wireless communication networkmay include multiple network nodes, shown as a network node (NN), a network node, a network node, and a network node. The network nodesmay support communications with multiple UEs, shown as a UE, a UE, a UE, a UE, and a UE.

110 120 100 100 100 100 The network nodesand the UEsof the wireless communication networkmay communicate using the electromagnetic spectrum, which may be subdivided by frequency or wavelength into various classes, bands, carriers, and/or channels. For example, devices of the wireless communication networkmay communicate using one or more operating bands. In some aspects, multiple wireless communication networksmay be deployed in a given geographic area. Each wireless communication networkmay support a particular RAT (which may also be referred to as an air interface) and may operate on one or more carrier frequencies in one or more frequency ranges. Examples of RATs include a 4G RAT, a 5G/NR RAT, and/or a 6G RAT, among other examples. In some examples, when multiple RATs are deployed in a given geographic area, each RAT in the geographic area may operate on different frequencies to avoid interference with one another.

25 7 125 52 6 71 52 6 114 25 300 6 30 300 6 6 100 4 5 Various operating bands have been defined as frequency range designations FR1 (410 MHz through 7.125 GHz), FR2 (24.GHz through 52.6 GHz), FR3 (.GHz through 24.25 GHz), FR4a or FR4-1 (.GHz throughGHz), FR4 (.GHz through 114.25 GHz), and FR5 (.GHz throughGHz). Although a portion of FR1 is greater thanGHz, FR1 is often referred to (interchangeably) as a “Sub-6 GHz” band in some documents and articles. Similarly, FR2 is often referred to (interchangeably) as a “millimeter wave” band in some documents and articles, despite being different than the extremely high frequency (EHF) band (GHz throughGHz), which is identified by the International Telecommunications Union (ITU) as a “millimeter wave” band. The frequencies between FR1 and FR2 are often referred to as mid-band frequencies, which include FR3. Frequency bands falling within FR3 may inherit FR1 characteristics or FR2 characteristics, and thus may effectively extend features of FR1 or FR2 into mid-band frequencies. Thus, “sub-6 GHz,” if used herein, may broadly refer to frequencies that are less thanGHz, that are within FR1, and/or that are included in mid-band frequencies. Similarly, the term “millimeter wave,” if used herein, may broadly refer to frequencies that are included in mid-band frequencies, that are within FR2, FR4, FR4-a or FR4-1, or FR5, and/or that are within the EHF band. Higher frequency bands may extend 5G NR operation,G operation, and/or other RATs beyond 52.6 GHz. For example, each of FR4a, FR4-1, FR4, and FR5 falls within the EHF band. In some examples, the wireless communication networkmay implement dynamic spectrum sharing (DSS), in which multiple RATs (for example,G/Long Term Evolution (LTE) andG/NR) are implemented with dynamic bandwidth allocation (for example, based on user demand) in a single frequency band. It is contemplated that the frequencies included in these operating bands (for example, FR1, FR2, FR3, FR4, FR4-a, FR4-1, and/or FR5) may be modified, and techniques described herein may be applicable to those modified frequency ranges.

110 120 100 110 5 6 A network nodemay include one or more devices, components, or systems that enable communication between a UEand one or more devices, components, or systems of the wireless communication network. A network nodemay be, may include, or may also be referred to as an NR network node, aG network node, aG network node, a Node B, an eNB, a gNB, an access point (AP), a transmission reception point (TRP), a mobility element, a core, a network entity, a network element, a network equipment, and/or another type of device, component, or system included in a radio access network (RAN).

110 110 110 110 100 110 120 100 A network nodemay be implemented as a single physical node (for example, a single physical structure) or may be implemented as two or more physical nodes (for example, two or more distinct physical structures). For example, a network nodemay be a device or system that implements part of a radio protocol stack, a device or system that implements a full radio protocol stack (such as a full gNB protocol stack), or a collection of devices or systems that collectively implement the full radio protocol stack. For example, and as shown, a network nodemay be an aggregated network node (having an aggregated architecture), meaning that the network nodemay implement a full radio protocol stack that is physically and logically integrated within a single node (for example, a single physical structure) in the wireless communication network. For example, an aggregated network nodemay consist of a single standalone base station or a single TRP that uses a full radio protocol stack to enable or facilitate communication between a UEand a core network of the wireless communication network.

110 110 110 Alternatively, and as also shown, a network nodemay be a disaggregated network node (sometimes referred to as a disaggregated base station), meaning that the network nodemay implement a radio protocol stack that is physically distributed and/or logically distributed among two or more nodes in the same geographic location or in different geographic locations. For example, a disaggregated network node may have a disaggregated architecture. In some deployments, disaggregated network nodesmay be used in an integrated access and backhaul (IAB) network, in an open radio access network (O-RAN) (such as a network configuration in compliance with the O-RAN Alliance), or in a virtualized radio access network (vRAN), also known as a cloud radio access network (C-RAN), to facilitate scaling by separating base station functionality into multiple units that can be individually deployed.

110 100 3 120 120 The network nodesof the wireless communication networkmay include one or more central units (CUs), one or more distributed units (DUs), and/or one or more radio units (RUs). A CU may host one or more higher layer control functions, such as RRC functions, packet data convergence protocol (PDCP) functions, and/or service data adaptation protocol (SDAP) functions, among other examples. A DU may host one or more of a radio link control (RLC) layer, a MAC layer, and/or one or more higher physical (PHY) layers depending, at least in part, on a functional split, such as a functional split defined by theGPP. In some examples, a DU also may host one or more lower PHY layer functions, such as a fast Fourier transform (FFT), an inverse FFT (iFFT), beamforming, physical random access channel (PRACH) extraction and filtering, and/or scheduling of resources for one or more UEs, among other examples. An RU may host RF processing functions or lower PHY layer functions, such as an FFT, an iFFT, beamforming, or PRACH extraction and filtering, among other examples, according to a functional split, such as a lower layer functional split. In such an architecture, each RU can be operated to handle over the air (OTA) communication with one or more UEs.

110 110 In some aspects, a single network nodemay include a combination of one or more CUs, one or more DUs, and/or one or more RUs. Additionally or alternatively, a network nodemay include one or more Near-Real Time (Near-RT) RAN Intelligent Controllers (RICs) and/or one or more Non-Real Time (Non-RT) RICs. In some examples, a CU, a DU, and/or an RU may be implemented as a virtual unit, such as a virtual central unit (VCU), a virtual distributed unit (VDU), or a virtual radio unit (VRU), among other examples. A virtual unit may be implemented as a virtual network function, such as associated with a cloud deployment.

110 3 110 110 110 110 120 120 120 120 110 110 110 110 Some network nodes(for example, a base station, an RU, or a TRP) may provide communication coverage for a particular geographic area. In theGPP, the term “cell” can refer to a coverage area of a network nodeor to a network nodeitself, depending on the context in which the term is used. A network nodemay support one or multiple (for example, three) cells. In some examples, a network nodemay provide communication coverage for a macro cell, a pico cell, a femto cell, or another type of cell. A macro cell may cover a relatively large geographic area (for example, several kilometers in radius) and may allow unrestricted access by UEswith service subscriptions. A pico cell may cover a relatively small geographic area and may allow unrestricted access by UEswith service subscriptions. A femto cell may cover a relatively small geographic area (for example, a home) and may allow restricted access by UEshaving association with the femto cell (for example, UEsin a closed subscriber group (CSG)). A network nodefor a macro cell may be referred to as a macro network node. A network nodefor a pico cell may be referred to as a pico network node. A network nodefor a femto cell may be referred to as a femto network node or an in-home network node. In some examples, a cell may not necessarily be stationary. For example, the geographic area of the cell may move according to the location of an associated mobile network node(for example, a train, a satellite base station, an unmanned aerial vehicle, or an NTN network node).

100 110 110 130 110 130 110 110 100 110 1 FIG. a a b b c The wireless communication networkmay be a heterogeneous network that includes network nodesof different types, such as macro network nodes, pico network nodes, femto network nodes, relay network nodes, aggregated network nodes, and/or disaggregated network nodes, among other examples. In the example shown in, the network nodemay be a macro network node for a macro cell, the network nodemay be a pico network node for a pico cell, and the network nodemay be a femto network node for a femto cell 130c.Various different types of network nodesmay generally transmit at different power levels, serve different coverage areas, and/or have different impacts on interference in the wireless communication networkthan other types of network nodes. For example, macro network nodes may have a high transmit power level (for example, 5 to 40 watts), whereas pico network nodes, femto network nodes, and relay network nodes may have lower transmit power levels (for example, 0.1 to 2 watts).

110 120 110 120 120 110 110 120 120 110 120 120 110 120 120 110 110 120 In some examples, a network nodemay be, may include, or may operate as an RU, a TRP, or a base station that communicates with one or more UEsvia a radio access link (which may be referred to as a “Uu” link). The radio access link may include a downlink and an uplink. “Downlink” (or “DL”) refers to a communication direction from a network nodeto a UE, and “uplink” (or “UL”) refers to a communication direction from a UEto a network node. Downlink channels may include one or more control channels and one or more data channels. A downlink control channel may be used to transmit downlink control information (DCI) (for example, scheduling information, reference signals, and/or configuration information) from a network nodeto a UE. A downlink data channel may be used to transmit downlink data (for example, user data associated with a UE) from a network nodeto a UE. Downlink control channels may include one or more physical downlink control channels (PDCCHs), and downlink data channels may include one or more physical downlink shared channels (PDSCHs). Uplink channels may similarly include one or more control channels and one or more data channels. An uplink control channel may be used to transmit uplink control information (UCI) (for example, reference signals and/or feedback corresponding to one or more downlink transmissions) from a UEto a network node. An uplink data channel may be used to transmit uplink data (for example, user data associated with a UE) from a UEto a network node. Uplink control channels may include one or more PUCCHs, and uplink data channels may include one or more physical uplink shared channels (PUSCHs). The downlink and the uplink may each include a set of resources on which the network nodeand the UEmay communicate.

120 120 110 120 100 120 100 120 120 120 120 120 Downlink and uplink resources may include time domain resources (frames, subframes, slots, and/or symbols), frequency domain resources (frequency bands, component carriers, subcarriers, resource blocks, and/or resource elements), and/or spatial domain resources (particular transmit directions and/or beam parameters). Frequency domain resources of some bands may be subdivided into bandwidth parts (BWPs). A BWP may be a continuous block of frequency domain resources (for example, a continuous block of resource blocks) that are allocated for one or more UEs. A UEmay be configured with both an uplink BWP and a downlink BWP (where the uplink BWP and the downlink BWP may be the same BWP or different BWPs). A BWP may be dynamically configured (for example, by a network nodetransmitting a DCI configuration to the one or more UEs) and/or reconfigured, which means that a BWP can be adjusted in real-time (or near-real-time) based on changing network conditions in the wireless communication networkand/or based on the specific requirements of the one or more UEs. This enables more efficient use of the available frequency domain resources in the wireless communication networkbecause fewer frequency domain resources may be allocated to a BWP for a UE(which may reduce the quantity of frequency domain resources that a UEis required to monitor), leaving more frequency domain resources to be spread across multiple UEs. Thus, BWPs may also assist in the implementation of lower-capability UEsby facilitating the configuration of smaller bandwidths for communication by such UEs.

100 110 110 110 110 110 110 110 110 110 110 110 110 120 As described above, in some aspects, the wireless communication networkmay be, may include, or may be included in, an IAB network. In an IAB network, at least one network nodeis an anchor network node that communicates with a core network. An anchor network nodemay also be referred to as an IAB donor (or “IAB-donor”). The anchor network nodemay connect to the core network via a wired backhaul link. For example, an Ng interface of the anchor network nodemay terminate at the core network. Additionally or alternatively, an anchor network nodemay connect to one or more devices of the core network that provide a core access and mobility management function (AMF). An IAB network also generally includes multiple non-anchor network nodes, which may also be referred to as relay network nodes or simply as IAB nodes (or “IAB-nodes”). Each non-anchor network nodemay communicate directly with the anchor network nodevia a wireless backhaul link to access the core network, or may communicate indirectly with the anchor network nodevia one or more other non-anchor network nodesand associated wireless backhaul links that form a backhaul path to the core network. Some anchor network nodeor other non-anchor network nodemay also communicate directly with one or more UEsvia wireless access links that carry access traffic. In some examples, network resources for wireless communication (such as time resources, frequency resources, and/or spatial resources) may be shared between access links and backhaul links.

110 110 120 120 110 100 110 110 120 110 120 120 120 120 1 FIG. d a d a d In some examples, any network nodethat relays communications may be referred to as a relay network node, a relay station, or simply as a relay. A relay may receive a transmission of a communication from an upstream station (for example, another network nodeor a UE) and transmit the communication to a downstream station (for example, a UEor another network node). In this case, the wireless communication networkmay include or be referred to as a “multi-hop network.” In the example shown in, the network node(for example, a relay network node) may communicate with the network node(for example, a macro network node) and the UEin order to facilitate communication between the network nodeand the UE. Additionally or alternatively, a UEmay be or may operate as a relay station that can relay transmissions to or from other UEs. A UEthat relays communications may be referred to as a UE relay or a relay UE, among other examples.

120 100 120 120 120 The UEsmay be physically dispersed throughout the wireless communication network, and each UEmay be stationary or mobile. A UEmay be, may include, or may be included in an access terminal, another terminal, a mobile station, or a subscriber unit. A UEmay be, include, or be coupled with a cellular phone (for example, a smart phone), a personal digital assistant (PDA), a wireless modem, a wireless communication device, a handheld device, a laptop computer, a cordless phone, a wireless local loop (WLL) station, a tablet, a camera, a gaming device, a netbook, a smartbook, an ultrabook, a medical device, a biometric device, a wearable device (for example, a smart watch, smart clothing, smart glasses, a smart wristband, and/or smart jewelry, such as a smart ring or a smart bracelet), an entertainment device (for example, a music device, a video device, and/or a satellite radio), an XR device, a vehicular component or sensor, a smart meter or sensor, industrial manufacturing equipment, a Global Navigation Satellite System (GNSS) device (such as a Global Positioning System device or another type of positioning device), a UE function of a network node, and/or any other suitable device or function that may communicate via a wireless medium.

120 110 A UEand/or a network nodemay include one or more chips, system-on-chips (SoCs), chipsets, packages, or devices that individually or collectively constitute or comprise a processing system. The processing system includes processor (or “processing”) circuitry in the form of one or multiple processors, microprocessors, processing units (such as central processing units (CPUs), graphics processing units (GPUs), neural processing units (NPUs) and/or digital signal processors (DSPs)), processing blocks, application-specific integrated circuits (ASIC), programmable logic devices (PLDs) (such as field programmable gate arrays (FPGAs)), or other discrete gate or transistor logic or circuitry (all of which may be generally referred to herein individually as “processors” or collectively as “the processor” or “the processor circuitry”). One or more of the processors may be individually or collectively configurable or configured to perform various functions or operations described herein. A group of processors collectively configurable or configured to perform a set of functions may include a first processor configurable or configured to perform a first function of the set and a second processor configurable or configured to perform a second function of the set, or may include the group of processors all being configured or configurable to perform the set of functions.

3 4 5 6 120 120 The processing system may further include memory circuitry in the form of one or more memory devices, memory blocks, memory elements or other discrete gate or transistor logic or circuitry, each of which may include tangible storage media such as random-access memory (RAM) or read-only memory (ROM), or combinations thereof (all of which may be generally referred to herein individually as “memories” or collectively as “the memory” or “the memory circuitry”). One or more of the memories may be coupled (for example, operatively coupled, communicatively coupled, electronically coupled, or electrically coupled) with one or more of the processors and may individually or collectively store processor-executable code (such as software) that, when executed by one or more of the processors, may configure one or more of the processors to perform various functions or operations described herein. Additionally or alternatively, in some examples, one or more of the processors may be preconfigured to perform various functions or operations described herein without requiring configuration by software. The processing system may further include or be coupled with one or more modems (such as a Wi-Fi (for example, Institute of Electrical and Electronics Engineers (IEEE) compliant) modem or a cellular (for example,GPPG LTE,G, orG compliant) modem). In some implementations, one or more processors of the processing system include or implement one or more of the modems. The processing system may further include or be coupled with multiple radios (collectively “the radio”), multiple RF chains, or multiple transceivers, each of which may in turn be coupled with one or more of multiple antennas. In some implementations, one or more processors of the processing system include or implement one or more of the radios, RF chains or transceivers. The UEmay include or may be included in a housing that houses components associated with the UEincluding the processing system.

120 120 120 100 Some UEsmay be considered machine-type communication (MTC) UEs, evolved or enhanced machine-type communication (eMTC), UEs, further enhanced eMTC (feMTC) UEs, or enhanced feMTC (efeMTC) UEs, or further evolutions thereof, all of which may be simply referred to as “MTC UEs”. An MTC UE may be, may include, or may be included in or coupled with a robot, an uncrewed aerial vehicle, a remote device, a sensor, a meter, a monitor, and/or a location tag. Some UEsmay be considered IoT devices and/or may be implemented as NB-IoT (narrowband IoT) devices. An IoT UE or NB-IoT device may be, may include, or may be included in or coupled with an industrial machine, an appliance, a refrigerator, a doorbell camera device, a home automation device, and/or a light fixture, among other examples. Some UEsmay be considered Customer Premises Equipment, which may include telecommunications devices that are installed at a customer location (such as a home or office) to enable access to a service provider's network (such as included in or in communication with the wireless communication network).

120 120 100 120 120 100 120 120 120 120 Some UEsmay be classified according to different categories in association with different complexities and/or different capabilities. UEsin a first category may facilitate massive IoT in the wireless communication network, and may offer low complexity and/or cost relative to UEsin a second category. UEsin a second category may include mission-critical IoT devices, legacy UEs, baseline UEs, high-tier UEs, advanced UEs, full-capability UEs, and/or premium UEs that are capable of URLLC, eMBB, and/or precise positioning in the wireless communication network, among other examples. A third category of UEsmay have mid-tier complexity and/or capability (for example, a capability between UEsof the first category and UEsof the second capability). A UEof the third category may be referred to as a reduced capacity UE (“RedCap UE”), a mid-tier UE, an NR-Light UE, and/or an NR-Lite UE, among other examples. RedCap UEs may bridge a gap between the capability and complexity of NB-IoT devices and/or eMTC UEs, and mission-critical IoT devices and/or premium UEs. RedCap UEs may include, for example, wearable devices, IoT devices, industrial sensors, and/or cameras that are associated with a limited bandwidth, power capacity, and/or transmission range, among other examples. RedCap UEs may support healthcare environments, building automation, electrical distribution, process automation, transport and logistics, and/or smart city deployments, among other examples.

120 120 120 110 120 120 120 110 120 120 110 120 100 120 110 a e a e a e In some examples, two or more UEs(for example, shown as UEand UE) may communicate directly with one another using sidelink communications (for example, without communicating by way of a network nodeas an intermediary). As an example, the UEmay directly transmit data, control information, or other signaling as a sidelink communication to the UE. This is in contrast to, for example, the UEfirst transmitting data in an UL communication to a network node, which then transmits the data to the UEin a DL communication. In various examples, the UEsmay transmit and receive sidelink communications using peer-to-peer (P2P) communication protocols, device-to-device (D2D) communication protocols, vehicle-to-everything (V2X) communication protocols (which may include vehicle-to-vehicle (V2V) protocols, vehicle-to-infrastructure (V2I) protocols, and/or vehicle-to-pedestrian (V2P) protocols), and/or mesh network communication protocols. In some deployments and configurations, a network nodemay schedule and/or allocate resources for sidelink communications between UEsin the wireless communication network. In some other deployments and configurations, a UE(instead of a network node) may perform, or collaborate or negotiate with one or more other UEs to perform, scheduling operations, resource selection operations, and/or other operations for sidelink communications.

110 120 100 110 120 110 120 110 120 110 120 110 120 120 110 120 110 110 110 120 110 120 120 110 120 In various examples, some of the network nodesand the UEsof the wireless communication networkmay be configured for full-duplex operation in addition to half-duplex operation. A network nodeor a UEoperating in a half-duplex mode may perform only one of transmission or reception during particular time resources, such as during particular slots, symbols, or other time periods. Half-duplex operation may involve time-division duplexing (TDD), in which DL transmissions of the network nodeand UL transmissions of the UEdo not occur in the same time resources (that is, the transmissions do not overlap in time). In contrast, a network nodeor a UEoperating in a full-duplex mode can transmit and receive communications concurrently (for example, in the same time resources). By operating in a full-duplex mode, network nodesand/or UEsmay generally increase the capacity of the network and the radio access link. In some examples, full-duplex operation may involve frequency-division duplexing (FDD), in which DL transmissions of the network nodeare performed in a first frequency band or on a first component carrier and transmissions of the UEare performed in a second frequency band or on a second component carrier different than the first frequency band or the first component carrier, respectively. In some examples, full-duplex operation may be enabled for a UEbut not for a network node. For example, a UEmay simultaneously transmit an UL transmission to a first network nodeand receive a DL transmission from a second network nodein the same time resources. In some other examples, full-duplex operation may be enabled for a network nodebut not for a UE. For example, a network nodemay simultaneously transmit a DL transmission to a first UEand receive an UL transmission from a second UEin the same time resources. In some other examples, full-duplex operation may be enabled for both a network nodeand a UE.

120 110 In some examples, the UEsand the network nodesmay perform MIMO communication. “MIMO” generally refers to transmitting or receiving multiple signals (such as multiple layers or multiple data streams) simultaneously over the same time and frequency resources. MIMO techniques generally exploit multipath propagation. MIMO may be implemented using various spatial processing or spatial multiplexing operations. In some examples, MIMO may support simultaneous transmission to multiple receivers, referred to as multi-user MIMO (MU-MIMO). Some RATs may employ advanced MIMO techniques, such as mTRP operation (including redundant transmission or reception on multiple TRPs), reciprocity in the time domain or the frequency domain, single-frequency-network (SFN) transmission, or non-coherent joint transmission (NC-JT).

120 140 140 140 In some aspects, the UEmay include a communication manager. As described in more detail elsewhere herein, the communication managermay obtain an indication that a model, associated with at least one of encoding or decoding, is to be used in association with a control channel; output, after obtaining the indication, one or more model parameters associated with a data distribution of the control channel; encode, using an encoder, data, the encoder being associated with the one or more model parameters; and output the data for transmission via the control channel. Additionally, or alternatively, the communication managermay perform one or more other operations described herein.

110 150 150 150 In some aspects, the network nodemay include a communication manager. As described in more detail elsewhere herein, the communication managermay output an indication that a model, associated with at least one of encoding or decoding, is to be used in association with a control channel; obtain, after obtaining the indication that the model is to be used, one or more model parameters associated with a data distribution of the control channel; obtain data associated with the control channel; and decode, using at least one of a decoder or an encoder, the data, the decoder and the encoder being associated with the one or more model parameters. Additionally, or alternatively, the communication managermay perform one or more other operations described herein.

1 FIG. 1 FIG. As indicated above,is provided as an example. Other examples may differ from what is described with regard to.

2 FIG. 110 120 is a diagram illustrating an example network nodein communication with an example UEin a wireless network, in accordance with the present disclosure.

2 FIG. 110 212 214 216 232 1 234 1 236 238 239 240 242 244 246 150 234 232 236 238 214 216 110 240 242 110 120 As shown in, the network nodemay include a data source, a transmit processor, a transmit (TX) MIMO processor, a set of modems(shown as 232a through 232t, where t ≥), a set of antennas(shown as 234a through 234v, where v ≥), a MIMO detector, a receive processor, a data sink, a controller/processor, a memory, a communication unit, a scheduler, and/or a communication manager, among other examples. In some configurations, one or a combination of the antenna(s), the modem(s), the MIMO detector, the receive processor, the transmit processor, and/or the TX MIMO processormay be included in a transceiver of the network node. The transceiver may be under control of and used by one or more processors, such as the controller/processor, and in some aspects in conjunction with processor-readable code stored in the memory, to perform aspects of the methods, processes, and/or operations described herein. In some aspects, the network nodemay include one or more interfaces, communication components, and/or other components that facilitate communication with the UEor another network node.

2 FIG. 2 FIG. 110 214 216 236 238 240 120 256 258 264 266 280 The terms “processor,” “controller,” or “controller/processor” may refer to one or more controllers and/or one or more processors. For example, reference to “a/the processor,” “a/the controller/processor,” or the like (in the singular) should be understood to refer to any one or more of the processors described in connection with, such as a single processor or a combination of multiple different processors. Reference to “one or more processors” should be understood to refer to any one or more of the processors described in connection with. For example, one or more processors of the network nodemay include transmit processor, TX MIMO processor, MIMO detector, receive processor, and/or controller/processor. Similarly, one or more processors of the UEmay include MIMO detector, receive processor, transmit processor, TX MIMO processor, and/or controller/processor.

2 FIG. In some aspects, a single processor may perform all of the operations described as being performed by the one or more processors. In some aspects, a first set of (one or more) processors of the one or more processors may perform a first operation described as being performed by the one or more processors, and a second set of (one or more) processors of the one or more processors may perform a second operation described as being performed by the one or more processors. The first set of processors and the second set of processors may be the same set of processors or may be different sets of processors. Reference to “one or more memories” should be understood to refer to any one or more memories of a corresponding device, such as the memory described in connection with. For example, operation described as being performed by one or more memories can be performed by the same subset of the one or more memories or different subsets of the one or more memories.

110 120 214 120 120 212 214 120 120 110 120 120 214 214 For downlink communication from the network nodeto the UE, the transmit processormay receive data (“downlink data”) intended for the UE(or a set of UEs that includes the UE) from the data source(such as a data pipeline or a data queue). In some examples, the transmit processormay select one or more modulation and coding scheme (MCSs) for the UEin accordance with one or more channel quality indicators (CQIs) received from the UE. The network nodemay process the data (for example, including encoding the data) for transmission to the UEon a downlink in accordance with the MCS(s) selected for the UEto generate data symbols. The transmit processormay process system information (for example, semi-static resource partitioning information (SRPI)) and/or control information (for example, CQI requests, grants, and/or upper layer signaling) and provide overhead symbols and/or control symbols. The transmit processormay generate reference symbols for reference signals (for example, a cell-specific reference signal (CRS), a demodulation reference signal (DMRS), or a channel state information (CSI) reference signal (CSI-RS)) and/or synchronization signals (for example, a primary synchronization signal (PSS) or a secondary synchronization signals (SSS)).

216 232 232 232 232 232 232 234 The TX MIMO processormay perform spatial processing (for example, precoding) on the data symbols, the control symbols, the overhead symbols, and/or the reference symbols, if applicable, and may provide a set of output symbol streams (for example, T output symbol streams) to the set of modems. For example, each output symbol stream may be provided to a respective modulator component (shown as MOD) of a modem. Each modemmay use the respective modulator component to process (for example, to modulate) a respective output symbol stream (for example, for orthogonal frequency division multiplexing (OFDM)) to obtain an output sample stream. Each modemmay further use the respective modulator component to process (for example, convert to analog, amplify, filter, and/or upconvert) the output sample stream to obtain a time domain downlink signal. The modemsa throught may together transmit a set of downlink signals (for example, T downlink signals) via the corresponding set of antennas.

100 212 A downlink signal may include a DCI communication, a MAC-CE communication, an RRC communication, a downlink reference signal, or another type of downlink communication. Downlink signals may be transmitted on a PDCCH, a PDSCH, and/or on another downlink channel. A downlink signal may carry one or more transport blocks (TBs) of data. A TB may be a unit of data that is transmitted over an air interface in the wireless communication network. A data stream (for example, from the data source) may be encoded into multiple TBs for transmission over the air interface. The quantity of TBs used to carry the data associated with a particular data stream may be associated with a TB size common to the multiple TBs. The TB size may be based on or otherwise associated with radio channel conditions of the air interface, the MCS used for encoding the data, the downlink resources allocated for transmitting the data, and/or another parameter. In general, the larger the TB size, the greater the amount of data that can be transmitted in a single transmission, which reduces signaling overhead. However, larger TB sizes may be more prone to transmission and/or reception errors than smaller TB sizes, but such errors may be mitigated by more robust error correction techniques.

120 110 120 234 232 232 236 238 238 239 240 For uplink communication from the UEto the network node, uplink signals from the UEmay be received by an antenna, may be processed by a modem(for example, a demodulator component, shown as DEMOD, of a modem), may be detected by the MIMO detector(for example, a receive (Rx) MIMO processor) if applicable, and/or may be further processed by the receive processorto obtain decoded data and/or control information. The receive processormay provide the decoded data to a data sink(which may be a data pipeline, a data queue, and/or another type of data sink) and provide the decoded control information to a processor, such as the controller/processor.

110 246 120 246 120 120 246 120 120 The network nodemay use the schedulerto schedule one or more UEsfor downlink or uplink communications. In some aspects, the schedulermay use DCI to dynamically schedule DL transmissions to the UEand/or UL transmissions from the UE. In some examples, the schedulermay allocate recurring time domain resources and/or frequency domain resources that the UEmay use to transmit and/or receive communications using an RRC configuration (for example, a semi-static configuration), for example, to perform semi-persistent scheduling (SPS) or to configure a configured grant (CG) for the UE.

214 216 232 234 236 238 240 110 110 110 One or more of the transmit processor, the TX MIMO processor, the modem, the antenna, the MIMO detector, the receive processor, and/or the controller/processormay be included in an RF chain of the network node. An RF chain may include one or more filters, mixers, oscillators, amplifiers, analog-to-digital converters (ADCs), and/or other devices that convert between an analog signal (such as for transmission or reception via an air interface) and a digital signal (such as for processing by one or more processors of the network node). In some aspects, the RF chain may be or may be included in a transceiver of the network node.

110 244 244 110 244 120 244 In some examples, the network nodemay use the communication unitto communicate with a core network and/or with other network nodes. The communication unitmay support wired and/or wireless communication protocols and/or connections, such as Ethernet, optical fiber, common public radio interface (CPRI), and/or a wired or wireless backhaul, among other examples. The network nodemay use the communication unitto transmit and/or receive data associated with the UEor to perform network control signaling, among other examples. The communication unitmay include a transceiver and/or an interface, such as a network interface.

120 252 252 252 1 254 254 254 1 256 258 260 262 264 266 280 282 140 120 284 252 254 256 258 264 266 120 280 282 120 110 120 The UEmay include a set of antennas(shown as antennasa throughr, where r ≥), a set of modems(shown as modemsa throughu, where u ≥), a MIMO detector, a receive processor, a data sink, a data source, a transmit processor, a TX MIMO processor, a controller/processor, a memory, and/or a communication manager, among other examples. One or more of the components of the UEmay be included in a housing. In some aspects, one or a combination of the antenna(s), the modem(s), the MIMO detector, the receive processor, the transmit processor, or the TX MIMO processormay be included in a transceiver that is included in the UE. The transceiver may be under control of and used by one or more processors, such as the controller/processor, and in some aspects in conjunction with processor-readable code stored in the memory, to perform aspects of the methods, processes, or operations described herein. In some aspects, the UEmay include another interface, another communication component, and/or another component that facilitates communication with the network nodeand/or another UE.

110 120 252 110 254 254 254 254 256 254 258 120 260 120 280 For downlink communication from the network nodeto the UE, the set of antennasmay receive the downlink communications or signals from the network nodeand may provide a set of received downlink signals (for example, R received signals) to the set of modems. For example, each received signal may be provided to a respective demodulator component (shown as DEMOD) of a modem. Each modemmay use the respective demodulator component to condition (for example, filter, amplify, downconvert, and/or digitize) a received signal to obtain input samples. Each modemmay use the respective demodulator component to further demodulate or process the input samples (for example, for OFDM) to obtain received symbols. The MIMO detectormay obtain received symbols from the set of modems, may perform MIMO detection on the received symbols if applicable, and may provide detected symbols. The receive processormay process (for example, decode) the detected symbols, may provide decoded data for the UEto the data sink(which may include a data pipeline, a data queue, and/or an application executed on the UE), and may provide decoded control information and system information to the controller/processor.

120 110 264 262 120 280 258 280 110 120 110 For uplink communication from the UEto the network node, the transmit processormay receive and process data (“uplink data”) from a data source(such as a data pipeline, a data queue, and/or an application executed on the UE) and control information from the controller/processor. The control information may include one or more parameters, feedback, one or more signal measurements, and/or other types of control information. In some aspects, the receive processorand/or the controller/processormay determine, for a received signal (such as received from the network nodeor another UE), one or more parameters relating to transmission of the uplink communication. The one or more parameters may include a reference signal received power (RSRP) parameter, a received signal strength indicator (RSSI) parameter, a reference signal received quality (RSRQ) parameter, a CQI parameter, or a transmit power control (TPC) parameter, among other examples. The control information may include an indication of the RSRP parameter, the RSSI parameter, the RSRQ parameter, the CQI parameter, the TPC parameter, and/or another parameter. The control information may facilitate parameter selection and/or scheduling for the UEby the network node.

264 264 266 254 266 254 254 254 254 The transmit processormay generate reference symbols for one or more reference signals, such as an uplink DMRS, an uplink sounding reference signal (SRS), and/or another type of reference signal. The symbols from the transmit processormay be precoded by the TX MIMO processor, if applicable, and further processed by the set of modems(for example, for DFT-s-OFDM or CP-OFDM). The TX MIMO processormay perform spatial processing (for example, precoding) on the data symbols, the control symbols, the overhead symbols, and/or the reference symbols, if applicable, and may provide a set of output symbol streams (for example, U output symbol streams) to the set of modems. For example, each output symbol stream may be provided to a respective modulator component (shown as MOD) of a modem. Each modemmay use the respective modulator component to process (for example, to modulate) a respective output symbol stream (for example, for OFDM) to obtain an output sample stream. Each modemmay further use the respective modulator component to process (for example, convert to analog, amplify, filter, and/or upconvert) the output sample stream to obtain an uplink signal.

254 254 252 120 The modemsa throughu may transmit a set of uplink signals (for example, R uplink signals or U uplink symbols) via the corresponding set of antennas. An uplink signal may include a UCI communication, a MAC-CE communication, an RRC communication, or another type of uplink communication. Uplink signals may be transmitted on a PUSCH, a PUCCH, and/or another type of uplink channel. An uplink signal may carry one or more TBs of data. Sidelink data and control transmissions (that is, transmissions directly between two or more UEs) may generally use similar techniques as were described for uplink data and control transmission, and may use sidelink-specific channels such as a physical sidelink shared channel (PSSCH), a physical sidelink control channel (PSCCH), and/or a physical sidelink feedback channel (PSFCH).

252 234 2 FIG. One or more antennas of the set of antennasor the set of antennasmay include, or may be included within, one or more antenna panels, one or more antenna groups, one or more sets of antenna elements, or one or more antenna arrays, among other examples. An antenna panel, an antenna group, a set of antenna elements, or an antenna array may include one or more antenna elements (within a single housing or multiple housings), a set of coplanar antenna elements, a set of non-coplanar antenna elements, or one or more antenna elements coupled with one or more transmission or reception components, such as one or more components of. As used herein, “antenna” can refer to one or more antennas, one or more antenna panels, one or more antenna groups, one or more sets of antenna elements, or one or more antenna arrays. “Antenna panel” can refer to a group of antennas (such as antenna elements) arranged in an array or panel, which may facilitate beamforming by manipulating parameters of the group of antennas. “Antenna module” may refer to circuitry including one or more antennas, which may also include one or more other components (such as filters, amplifiers, or processors) associated with integrating the antenna module into a wireless communication device.

234 252 In some examples, each of the antenna elements of an antennaor an antennamay include one or more sub-elements for radiating or receiving radio frequency signals. For example, a single antenna element may include a first sub-element cross-polarized with a second sub-element that can be used to independently transmit cross-polarized signals. The antenna elements may include patch antennas, dipole antennas, and/or other types of antennas arranged in a linear pattern, a two-dimensional pattern, or another pattern. A spacing between antenna elements may be such that signals with a desired wavelength transmitted separately by the antenna elements may interact or interfere constructively and destructively along various directions (such as to form a desired beam). For example, given an expected range of wavelengths or frequencies, the spacing may provide a quarter wavelength, a half wavelength, or another fraction of a wavelength of spacing between neighboring antenna elements to allow for the desired constructive and destructive interference patterns of signals transmitted by the separate antenna elements within that expected range.

The amplitudes and/or phases of signals transmitted via antenna elements and/or sub-elements may be modulated and shifted relative to each other (such as by manipulating phase shift, phase offset, and/or amplitude) to generate one or more beams, which is referred to as beamforming. The term “beam” may refer to a directional transmission of a wireless signal toward a receiving device or otherwise in a desired direction. “Beam” may also generally refer to a direction associated with such a directional signal transmission, a set of directional resources associated with the signal transmission (for example, an angle of arrival, a horizontal direction, and/or a vertical direction), and/or a set of parameters that indicate one or more aspects of a directional signal, a direction associated with the signal, and/or a set of directional resources associated with the signal. In some implementations, antenna elements may be individually selected or deselected for directional transmission of a signal (or signals) by controlling amplitudes of one or more corresponding amplifiers and/or phases of the signal(s) to form one or more beams. The shape of a beam (such as the amplitude, width, and/or presence of side lobes) and/or the direction of a beam (such as an angle of the beam relative to a surface of an antenna array) can be dynamically controlled by modifying the phase shifts, phase offsets, and/or amplitudes of the multiple signals relative to each other.

120 110 120 110 24 64 128 Different UEsor network nodesmay include different numbers of antenna elements. For example, a UEmay include a single antenna element, two antenna elements, four antenna elements, eight antenna elements, or a different number of antenna elements. As another example, a network nodemay include eight antenna elements,antenna elements,antenna elements,antenna elements, or a different number of antenna elements. Generally, a larger number of antenna elements may provide increased control over parameters for beam generation relative to a smaller number of antenna elements, whereas a smaller number of antenna elements may be less complex to implement and may use less power than a larger number of antenna elements. Multiple antenna elements may support multiple-layer transmission, in which a first layer of a communication (which may include a first data stream) and a second layer of a communication (which may include a second data stream) are transmitted using the same time and frequency resources with spatial multiplexing.

2 FIG. 264 258 266 280 While blocks inare illustrated as distinct components, the functions described above with respect to the blocks may be implemented in a single hardware, software, or combination component or in various combinations of components. For example, the functions described with respect to the transmit processor, the receive processor, and/or the TX MIMO processormay be performed by or under the control of the controller/processor.

3 FIG. 300 300 110 300 320 320 350 360 370 350 370 310 330 330 340 340 120 120 340 is a diagram illustrating an example disaggregated base station architecture, in accordance with the present disclosure. One or more components of the example disaggregated base station architecturemay be, may include, or may be included in one or more network nodes (such one or more network nodes). The disaggregated base station architecturemay include a CU 310 that can communicate directly with a core networkvia a backhaul link, or that can communicate indirectly with the core networkvia one or more disaggregated control units, such as a Non-RT RICassociated with a Service Management and Orchestration (SMO) Frameworkand/or a Near-RT RIC(for example, via an E2 link). In some examples, for RAN, the Non-RT RICmay be designed to process a task in non-real time (e.g., more than 1 second control loop) while the Near-RT RICmay be designed to process a task in near-real time (e.g., less than 10 millisecond control loop). The CUmay communicate with one or more DUsvia respective midhaul links, such as via F1 interfaces. Each of the DUsmay communicate with one or more RUsvia respective fronthaul links. Each of the RUsmay communicate with one or more UEsvia respective RF access links. In some deployments, a UEmay be simultaneously served by multiple RUs.

300 310 330 340 370 350 360 Each of the components of the disaggregated base station architecture, including the CUs, the DUs, the RUs, the Near-RT RICs, the Non-RT RICs, and the SMO Framework, may include one or more interfaces or may be coupled with one or more interfaces for receiving or transmitting signals, such as data or information, via a wired or wireless transmission medium.

310 310 330 330 340 330 330 310 340 340 330 3 In some aspects, the CUmay be logically split into one or more CU user plane (CU-UP) units and one or more CU control plane (CU-CP) units. A CU-UP unit may communicate bidirectionally with a CU-CP unit via an interface, such as the E1 interface when implemented in an O-RAN configuration. The CUmay be deployed to communicate with one or more DUs, as necessary, for network control and signaling. Each DUmay correspond to a logical unit that includes one or more base station functions to control the operation of one or more RUs. For example, a DUmay host various layers, such as an RLC layer, a MAC layer, or one or more PHY layers, such as one or more high PHY layers or one or more low PHY layers. Each layer (which also may be referred to as a module) may be implemented with an interface for communicating signals with other layers (and modules) hosted by the DU, or for communicating signals with the control functions hosted by the CU. Each RUmay implement lower layer functionality. In some aspects, real-time and non-real-time aspects of control and user plane communication with the RU(s)may be controlled by the corresponding DU. In some examples, in O-RAN architecture, CU, DU and RU may have equivalent RAN nodes (e.g., O-CU, O-DU and O-RU). Also, O1 and E2 interfaces may be supported by O-RAN nodes and/orGPP defined nodes (e.g., CU and DU).

360 360 360 390 310 330 340 350 370 360 380 360 340 330 310 310 330 The SMO Frameworkmay support RAN deployment and provisioning of non-virtualized and virtualized network elements. For non-virtualized network elements, the SMO Frameworkmay support the deployment of dedicated physical resources for RAN coverage requirements, which may be managed via an operations and maintenance interface, such as an O1 interface. For virtualized network elements, the SMO Frameworkmay interact with a cloud computing platform (such as an open cloud (O-Cloud) platform) to perform network element life cycle management (such as to instantiate virtualized network elements) via a cloud computing platform interface, such as an O2 interface. A virtualized network element may include, but is not limited to, a CU, a DU, an RU, a non-RT RIC, and/or a Near-RT RIC. In some aspects, the SMO Frameworkmay communicate with a hardware aspect of a 4G RAN, a 5G NR RAN, and/or a 6G RAN, such as an open eNB (O-eNB), via an O1 interface. Additionally or alternatively, the SMO Frameworkmay communicate directly with each of one or more RUsvia a respective O1 interface. In some deployments, this configuration can enable each DUand the CUto be implemented in a cloud-based RAN architecture, such as a vRAN architecture. In some examples, the O1 interface may be communicatively coupled to O-RAN NFs (e.g., the CU(s)and DU(s)).

350 370 350 370 370 310 330 370 The Non-RT RICmay include or may implement a logical function that enables non-real-time control and optimization of RAN elements and resources, AI/ML workflows including model training and updates, and/or policy-based guidance of applications and/or features in the Near-RT RIC. The Non-RT RICmay be coupled to or may communicate with (such as via an A1 interface) the Near-RT RIC. The Near-RT RICmay include or may implement a logical function that enables near-real-time control and optimization of RAN elements and resources via data collection and actions via an interface (such as via an E2 interface) connecting one or more CUs, one or more DUs, and/or an O-eNB with the Near-RT RIC.

370 350 370 360 350 350 370 350 360 In some aspects, to generate AI/ML models to be deployed in the Near-RT RIC, the Non-RT RICmay receive parameters or external enrichment information from external servers. Such information may be utilized by the Near-RT RICand may be received at the SMO Frameworkor the Non-RT RICfrom non-network data sources or from network functions. In some examples, the Non-RT RICor the Near-RT RICmay tune RAN behavior or performance. For example, the Non-RT RICmay monitor long-term trends and patterns for performance and may employ AI/ML models to perform corrective actions via the SMO Framework(such as reconfiguration via an O1 interface) or via creation of RAN management policies (such as A1 interface policies).

360 350 370 360 350 370 360 350 370 360 350 370 360 370 350 370 360 350 370 360 350 370 350 370 360 In other aspects, the SMO frameworkmay include both of the Non-RT RICand the Near-RT RIC. In such examples, the SMO frameworkmay have the same input and output interfaces or different input and output interfaces for the Non-RT RICand the Near-RT RIC. For example, an application (e.g., an energy saving application, a traffic steering application) may process tasks for the near-real time scale and the non-real time scale. When the SMO frameworkincludes the Non-RT RICand the Near-RT RIC, the application does not need to have additional logic or circuit to coordinate two different RICs. In such examples, the SMO frameworkmay include a single RIC or multiple RICs to operate as the Non-RT RICand the Near-RT RIC. In some examples, the SMO frameworkmay directly access the Near-RT RICand coordinate with the Non-RT RICand the Near-RT RIC. For example, the SMO frameworkmay share policies that the Non-RT RICand the Near-RT RICenforce. In further examples, the SMO frameworkmay reuse functionality for the Non-RT RICand the Near-RT RICbecause some functionality is duplicate across the Non-RT RICand the Near-RT RIC(e.g., application management, service discovery, data discovery, data management, data collection from RAN). In some examples, the applications in SMO frameworkmay interwork with applications in Near-RT RIC without any dependence on A1 interface as the application may discover and communicate with each other without a special interface. In the converged SMO framework to merge functionalities of both the time scales (e.g., non-real time and near-real time), a specific SMO framework may be configured with specific capabilities during deployment. For example, some SMO frameworks may be configured to only have Non-RT control, only have Near-RT control, or have both of Non-RT control and Near-RT control.

110 240 110 120 280 120 310 330 340 3 240 110 280 120 310 330 340 1400 110 110 110 120 120 120 110 120 1 2 FIGS., 2 FIG. 14 FIG. 2 FIG. 2 FIG. The network node, the controller/processorof the network node, the UE, the controller/processorof the UE, the CU, the DU, the RU, or any other component(s) of, ormay implement one or more techniques or perform one or more operations associated with model management for control channel encoding or decoding, as described in more detail elsewhere herein. For example, the controller/processorof the network node, the controller/processorof the UE, any other component(s) of, the CU, the DU, or the RUmay perform or direct operations of, for example, processof, or other processes as described herein (alone or in conjunction with one or more other processors). In some aspects, the wireless node described herein is the network node, is included in the network node, and/or includes one or more components of the network nodeshown in. Additionally, or alternatively, the wireless node described herein is the UE, is included in the UE, and/or includes one or more components of the UEshown in. For example, as used herein, “wireless node” refers to the network nodeand/or the UE.

242 110 110 310 330 340 282 120 242 282 242 282 110 120 310 330 340 1400 14 FIG. The memorymay store data and program codes for the network node, the network node, the CU, the DU, or the RU. The memorymay store data and program codes for the UE. In some examples, the memoryor the memorymay include a non-transitory computer-readable medium storing a set of instructions (for example, code or program code) for wireless communication. The memorymay include one or more memories, such as a single memory or multiple different memories (of the same type or of different types). The memorymay include one or more memories, such as a single memory or multiple different memories (of the same type or of different types). For example, the set of instructions, when executed (for example, directly, or after compiling, converting, or interpreting) by one or more processors of the network node, the UE, the CU, the DU, or the RU, may cause the one or more processors to perform processof, or other processes as described herein. In some examples, executing instructions may include running the instructions, converting the instructions, compiling the instructions, and/or interpreting the instructions, among other examples.

4 FIG. 360 360 360 360 402 402 350 370 360 404 404 360 illustrates a block diagram showing an SMO framework communicating with a network and foundational service components according to aspects of this disclosure. The SMO frameworkis a component to configure and manage RAN Network Functions (NF) and collect all the events that are reported by different NFs. For example, the SMO frameworkmay collect data (measurements, configuration, fault, event stream, and/or logging) from the NFs (e.g., using the O1 interface). Additionally or alternatively, the SMO frameworkmay provide policies (e.g., for UE to change frequency) or data enrichment to the NFs (e.g., over the A1 interface). The SMO frameworkmay include SMO service componentsto provide SMO services (e.g., service management, data management, policy management, RAN analytics, service orchestration, topology & inventory, RAN NF orchestration and management, and/or AI/ML workflow). The SMO service componentsmay run on the Non-RT RICand/or the Near-RT RICto provide various functionalities (e.g., service management, data management, RAN NF operation administration and maintenance (OAM), policy management RAN analytics, service orchestration, topology & inventory, and/or AI/ML workflow). In some examples, the SMO frameworkmay include applications(rApps and/or xApps) to maximize the network’s operational efficiency. The applicationsmay utilize SMO services to implement a use case for network management to meet the requirements of a use case (e.g., slice service level agreement, mobility performance, and/or spectral efficiency optimization). The SMO frameworkmay be deployed on premises, on the cloud (e.g., telco cloud), or as-a-service to meet the end-user requirements.

360 406 406 406 408 406 360 404 360 404 360 360 406 406 360 360 406 406 404 360 360 404 404 406 In some examples, the SMO frameworkmay communicate with foundational service components. Foundational service componentsmay be utilized for collecting data (e.g., measurements, configuration, fault, event stream, and/or logging) and sending control policy or commands to the network. The foundation service componentsmay perform third-party functionalities, which are provided by a cloud platform(e.g., private cloud or public cloud). A third-party foundational service componentsmay provide a functionality on which the SMO frameworkand the applicationscan be built, e.g., logging service for containerized applications. Such a service may or may not provide functionality specific to mobile network management. Both the SMO internal services of the SMO frameworkand the applicationsmay utilize the foundational services for the operations. Although the functionalities are not implemented in the SMO framework, the SMO frameworkmay use the functionalities via the foundational service components. The foundational service componentsmay include an artificial intelligence or machine learning service component, a data processing and storage service component, a telemetry service component, a security service component, and/or any other suitable service components to be used in the SMO framework, and/or network functions. The SMO frameworkmay be configured to use the foundational service componentsand expose the foundational service componentsto the applicationsrunning in the SMO framework. Also, the SMO frameworkmay simplify the interface to communicate with the applicationssuch that a single request from the applicationmay be mapped to multiple operations on the foundational service components.

406 360 406 360 360 406 360 406 360 110 406 360 360 406 360 360 The foundational service componentsmay be logically and/or physically separated from the SMO framework. For example, the foundational service componentsmay be in the same cloud server as the SMO frameworkor a different cloud server from the SMO framework. In some examples, the foundational service componentsmay be in the same circuit and/or memory as the SMO framework. In other examples, the foundational service componentsmay be the different circuit and/or memory from the SMO frameworkin the one or more network nodes. In further examples, the foundational service componentsmay be in the same cloud server as the SMO frameworkbut in the different circuit and/or memory from the SMO framework. The foundational service componentsmay be in a separate server, which is different from the network node including the SMO framework. In other examples, the components may be logically separated from the SMO framework.

5 FIG. 360 502 402 502 404 360 504 406 404 360 504 502 404 504 502 504 360 504 404 404 360 504 502 illustrates a block diagram showing an SMO framework communicating with a foundational service component and a network function according to aspects of this disclosure. For example, the SMO frameworkmay include an AI/ML workflow componentamong the SMO service components. The AI/ML workflow componentmay operate as an interface between one or more applicationsin the SMO frameworkand an AI/ML foundational service componentin the foundational service components. For example, an applicationin the SMO frameworkmay request a request to run inference on an AI/ML model, which runs on a third-party AI/ML foundational service component. In such examples, the AI/ML workflow componentin the SMO framework is configured to orchestrate the processes between the applicationand the third-party AI/ML foundational service component. The AI/ML workflow componentmay transmit a request to the third-party AI/ML foundational service componentto deploy the AI/ML model and configure the data pipeline. For example, the SMO frameworkmay route the input data to the location (e.g., third-party AI/ML foundational service component) where the AI/ML model is deployed and provide the output data generated from the AI/ML model to the applicationrunning in the SMO framework. In some examples, the several steps to receive the model inference result, the applicationin the SMO frameworkmay transmit a single request to the AI/ML foundational service component. Additionally or alternatively, the AI/ML workflow componentmay manage AI/ML workflow (e.g., model registry, model training, model deployment and inference, model monitoring, model update or rollback, A/B testing, and/or canary deployment).

360 506 508 330 340 320 360 508 402 404 506 508 508 506 506 506 360 360 506 506 3 FIG. 3 FIG. 3 FIG. 3 FIG. In some examples, the SMO frameworkmay communicate with one or more network functionsusing a management function component. For example, the network function may include the CU 310 in, the DUin, the RUin, or any other suitable network functions to control connectivity and data transfer in the RAN. In other examples, the network function may include any suitable network function in the core networkin. Thus, the SMO frameworkmay be configured to operate on a RAN domain only, a core network domain only, or both of the RAN domain and the core network domain. In some examples, the management function componentof the SMO service componentsmay interconnect between the applicationand the network function. For example, the management function componentmay manage the AI/ML model in the network by updating the AI/ML model and/or monitoring the performance of the AI/ML model. In some examples, the management function componentmay be communicatively coupled to a management function of the network functionwhere the management function of the network functionis implemented as part of the network function. The SMO frameworkmay collect data (e.g., measurements, configuration of the current configuration of the network functions, any fault data, event streams or logging information), and based on the data, the SMO frameworkmay manage the network functionto control the wireless communications by providing configuration or policy to the network function.

6 FIG. 360 508 360 602 604 606 608 610 506 612 506 602 604 606 508 402 404 360 360 506 602 612 illustrates a block diagram showing an SMO framework to communicate with network functions according to aspects of this disclosure. Network functions may provide services or functions that the SMO frameworkcan use. For example, the network functions may provide performance management, configuration management, fault management, trace policy management (e.g., management of tracing capability over the network and UE), policy-based control, and/or intent-based management. In some examples, performance management, configuration management, and fault management may be part of a network management model (e.g., fault, configuration, accounting, performance, and security (FCAPS) model). The management functionin the SMO frameworkmay have components (e.g., performance management, configuration management, fault management, trace policy management(e.g., management of tracing capability over the network and UE), policy-based controlof the network functions, and/or intent-based managementof the network functions. In some examples, performance management, configuration management, and fault management) corresponding to the functions or services that the network functions provide. The components in the management functionmay include interfaces to communicatively couple the network functions to the SMO service componentsand/or applicationsin the SMO framework. Then, the SMO frameworkmay be exposed to the services the network functionsprovided using the interfaces-. For example, policy-based management or control may enable the service consumer to specify a policy to be followed (e.g., switching off a capacity cell when a sector level traffic metric is lower than a threshold). In another example, the intent-based management may allow the service consumer to specify a high-level objective without specifying the objective to be met (e.g., providing a minimum threshold and a maximum latency to a group of users).

7 7 FIGS.A-D show different deployment options of an SMO framework. For example, an SMO framework may include multiple SMO instances with different scopes or capabilities to communicate with each other. In this way, the SMO framework may be flexibly configured and use less memory and power resources than multiple SMO frameworks. In some examples, to include multiple SMO instances in the SMO framework, the SMO framework may configure multiple SMO instances with different configurations to be exposed to different interfaces.

7 FIG.A 7 FIG.A 3 FIG. 3 FIG. 7 FIG.A 360 360 702 704 702 350 350 704 370 370 702 704 702 704 360 360 360 is a block diagram to show time-scale-based SMO framework deployment according to aspects of this disclosure. In, the SMO frameworkmay be split into multiple SMO instances based on time scales. For example, the SMO frameworkmay include a first SMO instancefor non-real time scale and a second SMO instancefor near-real time scale. In some examples, the first SMO instancemay include a non-RT RICinand SMO service components, which operate on the non-RT RIC. The second SMO instancemay include a near-RT RICinand SMO service components, which operate on the near-RT RIC. In some examples, the first and second SMO instances,may be configured with interfaces to support operation at different time scales. The first SMO instancemay consume RAN FCAPS while the second SMO instancemay consume UE level real-time measurements. In other examples, an SMO instance may be similar to the SMO framework. In such examples,shows two different SMO frameworksto communicate each other and manage different time scales. In some examples, applications in the SMO frameworkmay discover the multiple SMO instances and determine SMO instances to operate.

7 FIG.B 7 FIG.B 360 360 712 714 712 714 712 714 is a block diagram to show domain-based SMO framework deployment according to aspects of this disclosure. In, the SMO frameworkmay be split into multiple SMO instances based on domains. For example, the SMO frameworkmay include a first SMO instanceto manage a RAN domain and a second SMO instanceto manage a core network domain. In some examples, the first and second SMO instances,may be configured with interfaces to support operation at different domains. For example, the first SMO instancemay consume RAN FCAPS while the second SMO instancemay consume core network FCAPS.

7 FIG.C 7 FIG.C 360 360 722 724 722 724 is a block diagram to show cell group-based SMO framework deployment according to aspects of this disclosure. In, the SMO frameworkmay be split into multiple SMO instances based on different parts or geographical regions of the network. For example, the SMO frameworkmay include a first SMO instanceto manage cell group A and a second SMO instanceto manage cell group B. Cell group A and B may be configured to have a group of cells. The groups of cells may be configured based on geographical regions. In some examples, the first and second SMO instances,may be configured with interfaces (e.g., RAN FCAPS) but configured to operate on different parts or geographical regions of the network (e.g., by configuring a group of cells). Thus, the multiple SMO instances may communicate each other but operate on different parts of the network.

7 FIG.D 7 FIG.D 360 402 404 402 404 360 732 734 732 734 732 734 is a block diagram to show capability-based SMO framework deployment according to aspects of this disclosure. In, the SMO frameworkmay be split into multiple SMO instances based on SMO service componentsor applications. For example, an SMO service componentor an applicationin the SMO frameworkmay discover capabilities of different SMO instances,and interact with the SMO instances,based on the capabilities of the SMO instances,.

360 360 702 704 722 724 360 7 7 FIGS.A-D In other examples, the SMO frameworkmay include multiple SMO instances using a combination of the deployment options of. For example, the SMO frameworkmay include a first SMO instancefor the non-real time scale, a second SMO instancefor the near-real time scale, a third SMO instancefor cell group A, and a fourth SMO instancefor cell group B. The SMO frameworkmay include multiple SMO instances based on any other combination of SMO deployment options.

8 FIG. 802 804 806 808 810 802 810 802 812 806 814 804 812 806 360 is a block diagram to show SMO framework deployment in a distributed network according to aspects of this disclosure. The distributed network may include a multi-cloud platform. For example, the distributed network may include a centralized data center, which has a central cloudand/or edge locations,, which are closer to the cell sites. Across the multi-cloud platform, a virtual private cloudmay be configured and defined. The virtual private cloud may create a single network that spans across multiple data centers and locations. Services may be available on the virtual private network. Although the SMO framework can be differently deployed, the services in the SMO framework may be available on the virtual private network and different data centers. In some examples, only one centralized SMO instancemay be deployed in the central could. In other examples, multiple SMO instances may be deployed, with a centralized SMO instance(e.g., non-real time scale) deployed in the central cloudand an SMO edge instancein the cell site edge. In further examples, multiple SMO instances may be deployed, with a centralized SMO instance(e.g., non-real time scale) deployed in a telco edge cloudand an SMO edge instancein the cell site edge. In this way, regardless of the various deployment options of the SMO framework, the services of the SMO framework are accessible across the data centers in the multi-cloud platform.

9 FIG. 360 360 902 360 360 502 360 360 360 360 360 is a sequence diagram to show an onboarding process of an AI/ML foundational service to integrate the foundational service to an SMO framework according to aspects of this disclosure. To onboard an external or third-party foundational service component in the SMO framework, the SMO frameworkmay collect information (e.g., endpoint, API specification, payload, encoding) about the foundational service component and register the foundational service component. In some examples, registering the foundational service component may indicate registering a management service in the service registry. In some examples, the foundational service may be translated before being registered in the service management component. In some examples, the SMO frameworkmay translate the foundational service component using a gateway pattern. For example, the SMO frameworkmay allow access to APIs from the foundational service component (e.g., using an AI/ML workflow component). In other examples, the SMO frameworkmay translate the foundational service component using an adapter pattern. For example, the SMO frameworkmay translate a data type (payload) or resource structure to be used in an API. In further examples, the SMO frameworkmay translate the foundational service component using a façade pattern. For example, the SMO frameworkmay register a simplified API that maps to a combination of multiple operations in the foundational service component. In even further examples, the SMO frameworkmay perform the translation across service types (e.g., Representational State Transfer API to or from message bus such as Kafka).

360 360 504 504 360 504 504 902 360 504 In some examples, the SMO frameworkmay register the management service related to the foundational service by internally generating information about the foundational service. For example, the SMO frameworkmay register the AI/ML foundational service componentusing an internal platform or manually triggered action. The AI/ML foundational service componentmay provide an external and third-party AI/ML inference functionality. In some examples, the SMO frameworkmay identify information about the AI/ML foundational service componentand register the AI/ML foundational service componentin the service management componentin the SMO frameworkthrough proprietary means. For example, an operator may directly register the AI/ML foundational service componentby generating or identifying an internal application programming interface (API) in the service registry.

360 504 360 504 502 360 902 360 502 504 504 902 360 In other examples, the SMO frameworkmay onboard the AI/ML foundational service componentby receiving the information about the foundational service from the foundational service component. For example, the SMO frameworkmay register the AI/ML foundational service componentbased on an API call from AI/ML workflow componentin the SMO frameworkto the service management componentin the SMO framework. For example, the AI/ML workflow componentmay receive information about the AI/ML foundational service componentand providing the information to the service management to register the AI/ML foundational service componentin the service management componentin the SMO framework.

10 FIG. 9 FIG. 1002 1004 1002 902 360 1002 is a sequence diagram to show integration of a foundational service to an SMO framework according to aspects of this disclosure. The sequence diagram includes two phases: a pre-condition phaseand a gateway functionality phase. In the pre-condition phase, a foundational service component may be registered in the service management componentof the SMO framework. In some examples, the pre-condition phasemay be similar to the onboarding process described in.

1004 1006 1008 1010 1004 1006 360 504 902 404 360 902 360 902 504 404 504 The gateway functionality phasemay include a service discovery step, a model discovery step, and/or a service invocation step. The gateway functionality phasemay provide an example technique to access the foundational service component. However, it is not limited to the gateway functionality to access the foundational service component. For example, any other suitable technique (e.g., using direct access, façade functionality, and/or adapter functionality). In the service discovery step, the SMO frameworkmay discover the AI/ML foundational service componentthrough the service registry in the service management component. For example, the applicationin the SMO frameworkmay transmit a service discovery request to the service management componentin the SMO framework. The service discovery request may be a request to a specific endpoint with no message body (no input). For example, in RESTful design, the service discovery request may be achieved by sending a GET message to a specific endpoint and resource. Optionally or alternatively, the request may contain filters (e.g., a filter by services that include AI/ML in the name). Then the service management componentmay identify the AI/ML foundational service componentin the service registry and transmit a service discovery response to the application. The response may contain a list of service profiles. For example, each service profile may have information such as service name, description, endpoint, supported protocols, and/or supported data formats. The service discovery response may include information about the AI/ML foundational service component.

1008 360 404 360 502 360 502 504 502 504 504 502 404 504 502 504 502 504 404 In the model discovery step, the SMO frameworkmay discover an AI/ML model for inference. For example, the applicationin the SMO frameworkmay transmit a model discover request to the AI/ML workflow componentin the SMO framework. The AI/ML workflow componentmay transmit the model discovery request to the AI/ML foundational service componentusing a third-party API. Then, the AI/ML workflow componentmay receive a model discovery response from the AI/ML foundational service componentusing the third-party API. Based on the model discovery response, from the AI/ML foundational service component, the AI/ML workflow componentmay transmit a response corresponding to the model discovery response to the application. In some examples, the response may include the AI/ML model or a list of AI/ML models, which are available or accessible in the AI/ML foundational service component. For example, the AI/ML workflow componentmay call a function with an input of the model discovery request to interact with the AI/ML foundational service component. Then, the AI/ML workflow componentmay receive a return with the model discovery response. In some examples, the model discovery response may include a list of available or accessible AI/ML models in the AI/ML foundational service component. In response to the model discovery response, the applicationmay select an AI/ML model for inference.

1010 360 504 404 404 360 502 360 504 504 504 504 360 404 504 404 502 502 404 In the service invocation step, the SMO frameworkmay obtain an inference response based on an inference input using the AI/ML model in the AI/ML foundational service component. For example, after the applicationreceives the model discovery response, the applicationin the SMO frameworkmay transmit a request to the AI/ML workflow componentin the SMO framework. The request may include a request to deploy the AI/ML model, an inference input to the AI/ML model, and/or an inference request to perform inference using the AI/ML model. Then, the AI/ML workflow may call a function (e.g., a third-party API) to transmit the request to the AI/ML foundational service component. In some examples, in response to the request to deploy the AI/ML model, the AI/ML foundational service componentmay deploy the AI/ML model in the network. To deploy the AI/ML model, the AI/ML foundational service componentmay integrate the AI/ML model in the network. Deployment of an AI/ML model may indicate that hardware and software allocations are made so that inference can be performed using the AI/ML model. For example, hardware resources may be allocated (e.g., GPU resources), software required to perform inference may be “loaded” (deployed). Software may include data processing (convert input data to features that can be used for inference), AI/ML model (architecture and parameters) inference (code that performs inference). Once an AI/ML model is deployed, optionally or alternatively, an additional software layer may be available that exposes an API (e.g., REST API). A consumer can make an inference request by calling that API. The API server then may pass the input to the software layer that perform data processing and inference. Once the output is available, API server may return the output to the consumer. In some examples, in response to the inference input and the inference request, the AI/ML foundational service componentmay perform inference. The inference may include providing the inference input to the AI/ML model and receiving an inference output from the AI/ML model. In some examples, the inference input and output may not be registered as data types in the data management component in the SMO framework. In such examples, the applicationmay provide the inference input and obtain inference output through the model inference API from the AI/ML foundational service component. In other examples, the data management component may manage the inference input and output data. In such examples, the applicationmay provide the inference input to the data management component, which converts the inference input to a data format that the AI/ML workflow componentcan use to call the model inference API. Similarly, the data management component may receive the inference output from the AI/ML workflow componentand convert to a data format that the applicationuses.

The AI/ML model may have different architectures (e.g., number of layers, type of layers, ordering of layers, connections between layers, hyperparameters for layers) to improve communications in the network. In some configurations, the AI/ML model may be structured as a single-layer perceptron network, in which a single layer of output nodes is used, and inputs are fed directly to the outputs by a series of weights. In other configurations, the AI/ML model can be structured as multilayer perceptron networks, in which the inputs are fed to one or more hidden layers before connecting to the output layer. As one example, the AI/ML model may be configured as a feedforward network, in which the connections between nodes do not form any loops in the network. As another example, the AI/ML model may be configured as a recurrent neural network (“RNN”), in which connections between nodes are configured to allow for previous outputs to be used as inputs while having one or more hidden states, which in some instances may be referred to as a memory of the RNN. RNNs are advantageous for processing time-series or sequential data. Examples of RNNs include long-short term memory (“LSTM”) networks, networks based on or using gated recurrent units (“GRUs”), or the like.

The AI/ML model may be structured with different connections between layers. In some instances, the layers are fully connected, in which each all of the inputs in one layer are connected to each of the outputs of the previous layer. Additionally or alternatively, neural networks can be structured with trimmed connectivity between some or all layers, such as by using skip connections, dropouts, or the like. In skip connections, the output from one layer jumps forward two or more layers in addition to, or in lieu of, being input to the next layer in the network. An example class of the AI/ML model that implement skip connections includes residual neural networks, such as ResNet. In a dropout layer, nodes are randomly dropped out (e.g., by not passing their output on to the next layer) according to a predetermined dropout rate. In some embodiments, the AI/ML model may be configured as a convolutional neural network (“CNN”), in which the network architecture includes one or more convolutional layers. Additionally or alternatively, the AI/ML model may use supervised learning or unsupervised learning to be configured as a trained model. The AI/ML model is not limited to the models described above, but any other suitable AI/ML model can be used to improve communications in the network.

11 FIG. 404 404 is a sequence diagram to show integration of a foundational service to an SMO framework according to aspects of this disclosure. The sequence diagram may include AI/ML model lifecycle management. For example, the applicationmay deploy version X of an AI/ML model and determine to train and deploy a new AI/ML model based on one or more factors (e.g., AI/ML model performance and/or RAN performance). In such examples, the retraining the AI/ML model may be orchestrated such that the applicationmay control each step to retrain the AI/ML model.

404 360 504 1006 404 360 1008 404 10 FIG. 10 FIG. In some examples, the applicationin the SMO frameworkmay discover the AI/ML foundational service componentas described in the service discovery phasein. Additionally or alternatively, the applicationin the SMO frameworkmay discover the AI/ML model as described in the model discovery stepin. In some examples, the applicationmay determine the AI/ML model for inference and/or a version of the AI/ML model based on the model discovery.

404 404 504 502 360 504 504 404 504 502 360 504 When the applicationdetermines a version of the AI/ML model, the applicationmay transmit a request to deploy the version of the AI/ML model to the AI/ML foundational service componentusing the AI/ML workflow componentin the SMO framework(e.g., using a third-party API to request model deployment to the AI/ML foundational service component). Based on the API call, the AI/ML foundational service componentmay deploy the version of the AI/ML model. Then, the applicationmay request to perform model performance monitoring to the AI/ML foundational service componentvia the AI/ML workflow componentin the SMO framework(e.g., using a third-party API to request model performance monitoring to the AI/ML foundational service component). In some examples, model performance monitoring may be monitoring of how well the AI/ML model performs on new input data. For supervised learning models, the performance may be measured by comparing the model output (prediction) with ground truth (if available). The performance metric may be the absolute difference, square of difference, etc. An AI/ML model may not perform well after deployment due to a variety of reasons. The input data statistics may have changed (data drift), e.g., the model is trained with few cases of low signal strength but in production many cases with low signal strength are seen. The environment may have changed since model was trained, e.g., consumer behavior changed at the start of COVID-19 pandemic resulting in high error rates in credit card fraud detection systems.

404 1104 1102 360 1102 The applicationmay retrieve RAN information (e.g., RAN measurements and/or RAN configuration) from the RANvia the data management componentin the SMO framework. Data management service in the data management componentmay provide common services for data discovery, request and delivery. The data management service may provide a data registry where data producers can register the data types that they produce. Data consumers may discover the registered data types and determine which data types would be useful for them. This may decouple data consumers from data producers. For example, data consumers may focus on the needed data type without worrying about data producers (e.g., regardless that two data producers produce the same data type or one data producer for a geographic region (WEST) and the other data producer for another geographic region (e.g., EAST)). Data request and subscriptions may be handled by the data management service to enable optimizations in data delivery. For example, if multiple consumers want to consume the same data type, the data producer could still produce each data sample once, but data management may manage the subscriptions and delivery of the same data sample to multiple consumers. In some examples, the RAN management function may act as a data producer and registers a data type for RAN performance measurements in the data management. Examples of a performance measurement may include the number of active users and/or DL PRB utilization. These are measured over a time period (e.g., 15 min). An application can discover data types and it may find that RAN performance measurements data type is available. It can send a request to data management to obtain the latest sample, or may even create a subscription for these performance measurements. In case of the request (one-time), Data Management can provide the performance measurement sample if it already has it, or it may obtain it from RAN management service and then provide it to the application.

1102 360 404 404 404 404 404 502 504 504 404 504 502 360 404 In some examples, the data management componentof the SMO frameworkmay receive and store the RAN measurements (e.g., using the processor or RIC) and provide the measurements to the application. Then, the applicationmay determine to retrain the AI/ML model based on the model performance and RAN measurements. For example, the applicationmay determine that the AI/ML model performance affects the RAN performance. For example, when the AI/ML model performance shows underperformance, the RAN measurements may also indicate underperformance of the RAN. In such examples, the applicationmay determine to retrain the AI/ML model. Then, the applicationmay transmit a request to retrain the AI/ML model to the AI/ML workflow component, which transmits the request to the AI/ML foundational service component(e.g., using a third-party API). The AI/ML foundational service componentmay retrain the AI/ML model based on the request. Then, the applicationmay transmit a request to deploy the updated version of the AI/ML model to the AI/ML foundational service componentusing the AI/ML workflow componentin the SMO framework. The applicationmay monitor the performance of the updated AI/ML model.

12 FIG. 12 FIG. 9 FIG. 1202 360 504 1202 360 504 504 360 504 504 502 360 504 is a sequence diagram to show integration of a foundational service to an SMO framework according to aspects of this disclosure.shows a use case using a network energy saving applicationin the SMO frameworkand the AI/ML foundational service componentto improve network energy usage. The network energy saving applicationin the SMO frameworkmay use the cloud AI/ML foundational service component. To use the AI/ML foundational service component, the SMO frameworkmay onboard or register the AI/ML foundational service component. In some examples, the registration of the AI/ML foundational service componentmay be similar to the onboarding process described in. Additionally or alternatively, the AI/ML workflow componentof the SMO frameworkmay register the AI/ML foundational service component.

504 902 1202 504 902 1202 504 504 1202 502 504 360 504 When the service of the AI/ML foundational service componentis registered in the service management component, the network energy saving applicationmay discover the AI/ML foundational service componentin the service management component. Then, the network energy saving applicationmay invoke a service based on the discovered AI/ML foundational service component. For example, the service invocation may call a third-party API to deploy an AI/ML model for inference in the AI/ML foundational service component. In some examples, the network energy saving applicationmay transmit a request to deploy the AI/ML model to the AI/ML workflow component, which translate the request to call the third party API to deploy the AI/ML model. In such way, the AI/ML model and the AI/ML foundational service componentmay be integrated into the SMO framework. Then, the AI/ML foundational service componentmay deploy the AI/ML model.

1202 504 360 502 1202 360 1202 504 360 1202 504 1202 502 360 Then, the network energy saving applicationmay request inference to the AI/ML foundational service componentusing a third-party API to improve performance of the network. The SMO frameworkprovides efficient and seamless data movement and data discovery using the AI/ML workflow component. For example, the network energy saving applicationmay transmit a request for inference to the AI/ML workflow component of the SMO framework. For example, the network energy saving applicationmay receive a request relating to use of an external application (e.g., an AI/ML model of the AI/ML foundational service component). In some examples, the request may be received from an entity, which is external to the SMO frameworkor generated in the network energy saving application. In some examples, the request may include input data to the AI/ML model of the AI/ML foundational service component. In other examples, the request may include an indication to use the AI/ML model. The network energy saving applicationmay transmit the request to the AI/ML workflow componentof the SMO framework.

502 1202 504 504 504 504 502 1204 504 502 1202 502 1102 360 502 1102 1102 502 1204 504 1102 The AI/ML workflow componentmay receive the request from the network energy saving applicationand transmit a second request corresponding to the request to the AI/ML foundational service component. For example, the second request may include a third-party function call or API to use the deployed AI/ML model for inference. The API may be defined in the AI/ML foundational service component. The API may also include the input data to be applied to the AI/ML model in the AI/ML foundational service component. In other examples, the input data to be applied to the deployed AI/ML model may be transmitted to the AI/ML foundational service componentfrom other component or entity. The AI/ML workflow componentmay communicate with another third-party foundational service component (e.g., data processing and storage foundational service component) to provide input data to the AI/ML foundational service componentfor inference. For example, when the AI/ML workflow componentreceives the request from the network energy saving application, the AI/ML workflow componentmay discover data in the data management componentof the SMO framework. For example, the AI/ML workflow componentmay create a data type for inference input data in the data management component(e.g., when the data management component is involved in collection and processing of inference input data) and a data type for inference output data in the data management component. Then, the AI/ML workflow componentmay configure a data path to the data processing and storage foundational service componentand/or the AI/ML foundational service componentbased on the data discovery with the data management component.

1202 1202 502 504 1204 504 504 504 1204 504 When data path is not configured, the applicationmay collect the input data for an AI/ML model. The applicationmay send the input data as part of inference request to AI/ML workflow component, which may forward the inference request (with the input data) to the AI/ML foundational service component. In such examples, data may traverse multiple services. Instead of application fetching the input data and then sending it to the AI/ML foundational service, a path for data flow may be created from the source of that data to the AI/ML foundational service to reduce the cost of data movement. For example, RAN performance and configuration data is written to a software platform (e.g., Kafka) (onto different topics). The input data for a specific AI/ML model may be created by reading data from the software platform, processing it, and then writing the model input data into a new topic (Z). Configuring a data path may mean that a rule may be set up such that data from topic Z is read and sent to AI/ML foundational service for an inference using model. Each time a new data sample is available in topic Z, the inference using model may be be triggered. In such examples, the application, data management, and AI/ML workflow may not be involved after the data path is set up. Based on the configured data path, the data processing and storage foundational service componentmay directly provide the input data for inference to the AI/ML foundational service component. In other examples, the configured data path may be shared by the AI/ML foundational service component. In such examples, the AI/ML foundational service componentmay access the input data in the data processing and storage foundational servicebased on the configured data path. Then, the AI/ML foundational service componentmay apply the input data to the AI/ML model.

504 1102 360 1202 1102 1202 1102 The AI/ML model may generate an AI/ML inference output and/or model performance data when the AI/ML foundational service componentapplies the input data to the AI/ML model. For example, the data management componentof the SMO frameworkmay receive the inference output and/or model performance data and provide the inference output and/or model performance data to the network energy saving application. In other examples, the data management componentmay store the inference output and/or model performance data, and the network energy saving applicationmay access the inference output and/or model performance data in the data management component.

1202 360 1202 504 502 1202 1204 504 504 360 1202 360 1202 In some examples, the network energy saving applicationof the SMO frameworkmay be deployed in operator networks to collect traffic data during the day. Then, the network energy saving applicationmay receive a request to predict data traffic at a certain time (e.g., at 11:00 pm, between 10:00 pm and 6:00 am, 2 hours later, or any other suitable time) and transmit the request to the AI/ML foundational service componentvia the AI/ML workflow component. In some examples, the request may include the collected traffic data during the data as well. In other examples, the network every saving applicationmay store the traffic data in the data processing and storage foundational service componentthat the AI/ML foundational service componentcan access. Then, the AI/ML foundational service componentmay apply the traffic data to the AI/ML model to predict the data traffic at the time based on the request. In such examples, the SMO frameworkand/or the network energy saving applicationmay transmit an instruction for wireless communication based on the result of the AI/ML model. In some examples, the result of the AI/ML model include inference using the AI/ML model. For example, when the predicted data traffic is low at the certain time, the SMO frameworkand/or the network energy saving applicationmay transmit an instruction to switch off some cells on a tower covering the same geographical area and maintain the other cells on the tower. The network traffic may not just be controlled based on a historical pattern but based on the current data traffic. Thus, the network energy on the tower can be saved based on the data traffic on the network.

360 In some examples, the SMO frameworkmay de-configure the data pipeline and remove the deployed AI/ML model when the AI/ML model is no longer used (e.g., when all the services of the application that requested model deployment for the specific model have notified that the application does not use the inference).

13 FIG. 14 FIG. 1 3 FIGS.- 2 FIG. 1300 1300 1400 1300 105 1300 702 704 706 1300 240 242 1300 1300 1300 240 1301 234 1301 105 232 220 230 236 238 is a block diagram of an example network nodethat SMO framework configuration for external functionalities according to one or more aspects. The network nodemay be configured to perform operations, including the blocks of the processdescribed with reference to, respectively. In some implementations, the network nodeincludes the one or more chips, SoCs, chipsets, packages, structure, hardware, and components shown and described with reference to the network nodeof. Additionally or alternatively, the network nodemay be included in the central server, the telco edge cloud server, and/or the cell site edge server. For example, the network nodemay include the controller, which operates to execute logic or computer instructions stored in the memory, as well as controlling the components of the network nodethat provide the features and functionality of the network node. The network node, under control of the controller, transmits and receives signals via wireless radiosa-t and the antennasa-t. The wireless radiosa-t include various components and hardware, as illustrated infor the network node, including the modulator and demodulatorsa-t, the transmit processor, the TX MIMO processor, the MIMO detector, and the receive processor.

242 360 1302 1304 360 1202 902 1102 502 360 1300 702 704 706 360 240 1302 360 1304 1300 1204 504 1104 3 FIG. 4 FIG. 12 FIG. 9 FIG. 11 FIG. 5 FIG. 7 FIG. 12 FIG. 5 FIG. 11 FIG. As shown, the memorymay include an SMO frameworkin, a configuration generation logic, and a transceiving logic. The SMO frameworkmay the application 404 in, the network energy saving applicationin, the service management componentin, the data management componentin, and/or the AI/ML workflow componentin. The SMO frameworkmay be deployed in one network node, a central serverin, a telco edge server, and/or a cell site edge server. In some examples, the SMO frameworkmay also include a hardware component (e.g., controllerand/or wireless radios 1301a-t). The configuration generation logicmay configure and orchestrate the SMO frameworkto communicate with external entity and use external functionalities. The transceiving logicof the network nodemay be configured to transmit signals (e.g., receiving a first request relating to use of an external application, transmitting a second request relating to performance of the external application, receiving an indication of a result corresponding to the second request, transmitting an instruction for wireless communication based on the indication, and/or transmitting or receiving any other suitable signals) to one or more external entities (e.g., the data processing and storage foundational service componentin, the AI/ML foundational service componentin, the RANin.

1300 1400 1300 240 1302 1304 242 1302 1402 1304 1402 1404 14 FIG. 14 FIG. In some implementations, the network nodemay be configured to perform the processof. To illustrate, the network nodemay execute, under control of the controller(e.g., Non-RT RIC and/or Near-RT RIC), the configuration generation logicand the transceiving logicstored in the memory. The execution environment of the configuration generation logicprovides the functionality to perform at least the operations in block. The execution environment of the transceiving logicprovides the functionality to perform at least the operations in blocksandin.

14 FIG. 1 3 FIGS.- 13 FIG. 1400 1300 illustrates a methodfor wireless communication at a network node according to aspects of this disclosure. According to some aspects, the network node is a network node is a network entity, such as a base station as described in any ofand the network nodein.

1402 360 360 404 406 504 3 13 FIGS.- 4 FIG. 4 5 FIGS., 4 FIG. 5 9 12 FIGS.and- 9 12 FIGS.- At block, the network node transmits, by an SMO framework from a first entity, a first request relating to use of an external application. In some examples, the SMO framework is similar to the SMO frameworkin. The first entity may include an application in the SMO framework. The application may be similar to rApp or xApp in, the applicationin, and 9-12. The external application may be in a second entity, which is a third-party foundational service component providing external functionalities. For example, the foundational service component may be similar to the foundational service componentin(e.g., AI/ML foundational service componentin). The foundational service component may be in the same or different cloud server. In some examples, the external application comprises an artificial intelligence or machine learning (AI/ML) model. The AI/ML model is similar to the AI/ML model in.

504 9 FIG. 10 FIG. In some examples, the network node may register the external application. In some examples, the registering of the external application may be similar to the onboarding process of the AI/ML foundational service componentin. For example, the network node may retrieve information associated with the external application in a memory to register the external application or receive the information associated with the external application from the second entity to register the external application. Additionally or alternatively, the network node may identify the external application using a discovery operation in the SMO framework. In some examples, the identifying of the external application is similar to the service discovery in.

7 7 8 FIGS.A-D and 3 FIG. In some examples, the SMO framework may be configured to operate on a radio access network domain only, a core network domain only, or both of the radio access network domain and the core network domain. In other examples, the SMO framework may be configured to operate on a specific function or a scope of the wireless communications. In some examples, the configuration of the SMO framework is similar to the deployment of the SMO framework in. Additionally or alternatively, the SMO framework comprises a non-real time intelligent controller (Non-RT RIC) and near-real time intelligent controller (Near-RT RIC) to support different time scale operations. In some examples, the controller in the SMO framework may be similar to the Non-RT RIC and the Near-RT RIC in.

1404 406 504 502 502 4 FIG. 5 9 12 FIGS.and- 10 12 FIGS.- At block, the network node transmits, by the SMO framework to a second entity, a second request relating to performance of the external application. In some examples, the second request may be transmitted by routing the first request or mapping the first request to the second request to cause the second entity to receive. In some examples, the second entity may be similar to the foundational service componentin(e.g., AI/ML foundational service componentin). Also, the second request may be similar to the request or the third-party API transmitted by the AI/ML workflow componentto the AI/ML foundational service componentin. The second request may also include the AI/ML inference output data and/or model performance.

1204 12 FIG. 12 FIG. In some examples, the network node may configure a data path based on the second request for input data to be transmitted by a third entity to the second entity and transmit the data path to the third entity. The indication of the result may be in response to the input data from the third entity. In some examples, the third entity may be similar to the data processing and storage foundational service componentinor any other suitable third-party foundational service component. The data path configuration may be similar to the data discovery, the data path configuration, and AI/ML inference input data transmission in.

10 12 FIGS.- 10 12 FIGS.- 11 FIG. 11 FIG. When the external application is the AI/ML model, the network node may transmit a third request to deploy the AI/ML model to the second entity and receive endpoint information for accessing the deployed AI/ML model in response to the third request. In some examples, the third request may be the request to deploy the AI/ML model inIn further examples, the second request may include inference input data for the AI/ML model. The inference input data may be similar to the inference input data in. In such examples, the network node may determine the inference input data based on data collected from the wireless communications and determine an output data type for the indication of the result of the AI/ML model. For example, the data collected from the wireless communications may be similar to RAN measurements in. Additionally or alternative, the network node may determine at least one measurement of the wireless communications or the AI/ML model, determine a training request of the AI/ML model or a new model based on the at least one measurement, and transmit the training request of the AI/ML model or the new model to the second entity. In some examples, the AI/ML model training is similar to the model training and deployment in.

1406 10 12 FIGS.- At step, the network node receives, by the SMO framework from the second entity, an indication of a result corresponding to the second request transmitted to the second entity. The indication of the result may be similar to the AI/ML inference output and/or model performance data in.

1408 12 FIG. At step, the network node transmits an instruction for wireless communication based on the indication. In some examples, the instruction for wireless communication is similar to the instruction in.

Implementation examples are described in the following numbered clauses:

1 Clause: A method for wireless communication, the method comprising: receiving, by an SMO framework from a first entity, a first request relating to use of an external application; transmitting, by the SMO framework to a second entity, a second request relating to performance of the external application; receiving, by the SMO framework from the second entity, an indication of a result corresponding to the second request transmitted to the second entity; and transmitting an instruction for wireless communication based on the indication.

2 1 Clause: The method of Clause, further comprising: configuring a data path between the third entity and the second entity based on the second request for input data to be transmitted by a third entity to the second entity; and transmitting the data path to the third entity, the indication of the result being in response to the input data from the third entity.

3 1 2 Clause: The method of Clauseor, wherein the second request is transmitted by routing the first request or mapping the first request to the second request to cause the second entity to receive.

4 1 3 Clause: The method of one or more of Clausethrough Clause, wherein the external application comprises an artificial intelligence or machine learning (AI/ML) model, and wherein the method further comprises: transmitting a third request to deploy the AI/ML model to the second entity; and in response to the third request, receiving endpoint information for accessing the deployed AI/ML model.

5 1 4 Clause: The method of one or more of Clausethrough Clause, wherein the second request comprises inference input data for the AI/ML model, and wherein the method further comprises: determining the inference input data based on data collected from the wireless communications; and determining an output data type for the indication of the result of the AI/ML model.

6 1 5 Clause: The method of one or more of Clausethrough Clause, further comprising: determining at least one measurement of the wireless communications or the AI/ML model; determining a training request of the AI/ML model or a new model based on the at least one measurement; and transmitting the training request of the AI/ML model or the new model to the second entity.

7 1 6 Clause: The method of one or more of Clausethrough Clause, wherein the SMO framework is configured to operate on a radio access network domain only, a core network domain only, or both of the radio access network domain and the core network domain.

8 1 7 Clause: The method of one or more of Clausethrough Clause, wherein the SMO framework is configured to operate on a specific function or a scope of the wireless communications.

1 8 Clause 9: The method of one or more of Clausethrough Clause, further comprising: registering the external application, wherein registering the external application comprises: retrieving information associated with the external application in a memory to register the external application, or receiving the information associated with the external application from the second entity to register the external application.

10 1 9 Clause: The method of one or more of Clausethrough Clause, further comprising: identifying the external application using a discovery operation in the SMO framework.

11 1 10 Clause: The method of one or more of Clausethrough Clause, wherein the SMO framework comprises a non-real time intelligent controller (Non-RT RIC) and near-real time intelligent controller (Near-RT RIC) to support different time scale operations.

12 1 11 Clause: An apparatus configured to operate as a Service Management and Orchestration (SMO) framework, the apparatus comprising: at least one processor to configure the SMO framework to perform operations comprising: the method of one or more of Clausethrough Clause.

13 1 11 Clause: A computer-readable storage medium that stores instructions for execution by one or more processors of a Service Management and Orchestration (SMO) framework, the instructions to configure the SMO framework to perform operations comprising: the method of one or more of Clausethrough Clause.

In the figures, a single block may be described as performing a function or functions. The function or functions performed by that block may be performed in a single component or across multiple components, or may be performed using hardware, software, or a combination of hardware and software. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps are described below generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of this disclosure. Also, the example devices may include components other than those shown, including well-known components such as a processor, memory, and the like.

In some cases, rather than actually transmitting a signal, an apparatus (e.g., a wireless node or device) may have an interface to output the signal for transmission. For example, a processor may output a signal, via a bus interface, to a radio frequency (RF) front end for transmission. Accordingly, a means for outputting may include such an interface as an alternative (or in addition) to a transmitter or transceiver. Similarly, rather than actually receiving a signal, an apparatus (e.g., a wireless node or device) may have an interface to obtain a signal from another device. For example, a processor may obtain (or receive) a signal, via a bus interface, from an RF front end for reception. Accordingly, a means for obtaining may include such an interface as an alternative (or in addition) to a receiver or transceiver.

While the present disclosure may describe certain operations as being performed by one type of wireless node, the same or similar operations may also be performed by another type of wireless node. For example, operations performed by a user equipment (UE) may also (or instead) be performed by a network entity (e.g., a base station or unit of a disaggregated base station). Similarly, operations performed by a network entity may also (or instead) be performed by a UE.

Further, while the present disclosure may describe certain types of communications between different types of wireless nodes (e.g., between a network entity and a UE), the same or similar types of communications may occur between same types of wireless nodes (e.g., between network entities or between UEs, in a peer-to-peer scenario). Further, communications may occur in reverse order than described.

As used herein, the term “determine” or “selecting” encompasses a wide variety of actions and, therefore, “selecting” can include calculating, computing, processing, deriving, estimating, investigating, looking up (such as via looking up in a table, a database, or another data structure), inferring, ascertaining, or measuring, among other possibilities. Also, “selecting” can include receiving (such as receiving information), accessing (such as accessing data stored in memory) or transmitting (such as transmitting information), among other possibilities. Additionally, “selecting” can include resolving, selecting, obtaining, choosing, establishing and other such similar actions.

As used herein, a phrase referring to “at least one of” or “one or more of” a list of items refers to any combination of those items, including single members. As an example, “at least one of: a, b, or c” is intended to cover: a, b, c, a-b, a-c, b-c, and a-b-c. As used herein, “or” is intended to be interpreted in the inclusive sense, unless otherwise explicitly indicated. For example, “a or b” may include a only, b only, or a combination of a and b. Furthermore, as used herein, a phrase referring to “a” or “an” element refers to one or more of such elements acting individually or collectively to perform the recited function(s). Additionally, a “set” refers to one or more items, and a “subset” refers to less than a whole set, but non-empty.

As used herein, “based on” is intended to be interpreted in the inclusive sense, unless otherwise explicitly indicated. For example, “based on” may be used interchangeably with “based at least in part on,” “associated with,” “in association with,” or “in accordance with” unless otherwise explicitly indicated. Specifically, unless a phrase refers to “based on only ‘a,’” or the equivalent in context, whatever it is that is “based on ‘a,’” or “based at least in part on ‘a,’” may be based on “a” alone or based on a combination of “a” and one or more other factors, conditions, or information.

The various illustrative components, logic, logical blocks, modules, circuits, operations, and algorithm processes described in connection with the examples disclosed herein may be implemented as electronic hardware, firmware, software, or combinations of hardware, firmware, or software, including the structures disclosed in this specification and the structural equivalents thereof. The interchangeability of hardware, firmware and software has been described generally, in terms of functionality, and illustrated in the various illustrative components, blocks, modules, circuits and processes described above. Whether such functionality is implemented in hardware, firmware or software depends upon the particular application and design constraints imposed on the overall system.

Various modifications to the examples described in this disclosure may be readily apparent to persons having ordinary skill in the art, and the generic principles defined herein may be applied to other examples without departing from the spirit or scope of this disclosure. Thus, the claims are not intended to be limited to the examples shown herein, but are to be accorded the widest scope consistent with this disclosure, the principles and the novel features disclosed herein.

Additionally, various features that are described in this specification in the context of separate examples also can be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation also can be implemented in multiple examples separately or in any suitable sub-combination. As such, although features may be described above as acting in particular combinations, and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. Further, the drawings may schematically depict one or more example processes in the form of a flowchart or flow diagram. However, other operations that are not depicted can be incorporated in the example processes that are schematically illustrated. For example, one or more additional operations can be performed before, after, simultaneously, or between any of the illustrated operations. In some circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the examples described above should not be understood as requiring such separation in all examples, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.